An organization's contact center typically receives a multitude of communications or interactions (e.g., calls, text chat messages, email messages, social media messages, etc.) regarding a variety of issues. For example, a sales department of a contact center may take part in interactions involving questions about the feature sets and pricing of various products offered by the organization; a customer support department may interact with customers to discuss particular problems with using the products or the quality of the services being delivered; an accounts department may field interactions about changes in billing policy, incorrect charges, and other issues.
Generally, it is useful for an organization to be able to identify concepts and patterns within the conversations (or “interactions”) in order to categorize the calls and identify underlying issues to be addressed (e.g., specific complaints about products or general dissatisfaction with services). However, conventional systems for doing so generally involve the manual survey of data collected by customer support agents and manual analysis of this data. This manual process of analysis can be time consuming and there may be long delays between collecting the data and determining results from the analysis.
In some conventional systems, conversations can be tagged or categorized based on their containing predefined keywords or phrases. For example, through the above discussed manual (human) analysis of phrases that are either identified by a human listener or identified by a computer system using phrase recognition, one might infer that conversations with a call center that contain the phrases “I would like to speak to your manager” and “Can I talk to your supervisor?” lead to the escalation of the call to a higher level representative. As such, any call containing these phrases would be categorized as containing an “escalation attempt.”
As such, an organization can identify trends and infer conditions based on the number of such interactions falling into various categories. For example, a large number of interactions originating from a particular area and categorized as indicating a “service outage” or “poor network performance” could alert an internet service provider to take action to address system problems within that particular area.
However, conversations containing phrases that were not previously identified would not be categorized appropriately. For example, if the phrase “Let me talk to your boss” was not previously identified as being associated with escalation attempts, then a conversation containing that phrase would not be categorized as an “escalation attempt.”
In addition, some conventional systems use Bayesian networks to identify correlations between events. However, developing these Bayesian networks requires significant human input to specify various parameters (e.g., the nodes of the Bayesian network).